Data Fields | |
| double | delta |
| double | dim_cycle_tolerance |
| int | iters_fixed_T |
| double | k |
| int | max_iterations |
| char * | method |
| double | mu_t |
| int | n_tries |
| apop_data ** | path |
| gsl_rng * | rng |
| double * | starting_pt |
| double | step_size |
| double | t_initial |
| double | t_min |
| double | tolerance |
| int | verbose |
The settings for maximum likelihood estimation (including simulated annealing).
| double apop_mle_settings::dim_cycle_tolerance |
If zero (the default), the usual procedure. If 
, cycle across dimensions: fix all but the first dimension at the starting point, optimize only the first dim. Then fix the all but the second dim, and optimize the second dim. Continue through all dims, until the log likelihood at the outset of one cycle through the dimensions is within this amount of the previous cycle's log likelihood. There will be at least two cycles.
| int apop_mle_settings::max_iterations |
Ignored by simulated annealing. Other methods halt (and set the "status" element of the output estimate's info page) if they do this many iterations without finding an optimum.
| char * apop_mle_settings::method |
The method to be used for the optimization. All strings are case-insensitive.
| String | Name | Notes |
| "NM simplex" | Nelder-Mead simplex | Does not use gradients at all. Can sometimes get stuck. |
| "FR cg" | Conjugate gradient (Fletcher-Reeves) (default) | CG methods use derivatives. The converge to the optimum of a quadratic function in one step; performance degrades as the objective digresses from quadratic. |
| "BFGS cg" | Broyden-Fletcher-Goldfarb-Shanno conjugate gradient | |
| "PR cg" | Polak-Ribiere conjugate gradient | |
| "Annealing" | simulated annealing | Slow but works for objectives of arbitrary complexity, including stochastic objectives. |
| "Newton" | Newton's method | Search by finding a root of the derivative. Expects that gradient is reasonably well-behaved. |
| "Newton hybrid" | Newton's method/gradient descent hybrid | Find a root of the derivative via the Hybrid method If Newton proposes stepping outside of a certain interval, use an alternate method. See the GSL manual for discussion. |
| "Newton hybrid no scale" | Newton's method/gradient descent hybrid with spherical scale | As above, but use a simplified trust region. |
| apop_data ** apop_mle_settings::path |
If not NULL, record each vector tried by the optimizer as one row of this apop_data set. Each row of the matrix element holds the vector tried; the corresponding element in the vector is the evaluated value at that vector (after out-of-constraints penalties have been subtracted). A new apop_data set is allocated at the pointer you send in. This data set has no names; add them as desired. For a sample use, see Optimization.
| double * apop_mle_settings::starting_pt |
An array of doubles (e.g., (double*){2,4,6,8}) suggesting a starting point. If NULL, use an all-ones vector. If startv is a gsl_vector and is not a view of a matrix, use .starting_pt=startv->data.
| double apop_mle_settings::step_size |
The initial step size.
| double apop_mle_settings::tolerance |
The precision the minimizer uses in its stopping rule. Only vaguely related to the precision of the actual MLE.
| int apop_mle_settings::verbose |
Give status updates as we go. This is orthogonal to the apop_opts.verbose setting.
